Detecting Cutleaf Teasel (Dipsacus laciniatus) along a Missouri Highway with Hyperspectral Imagery

2012 ◽  
Vol 5 (2) ◽  
pp. 155-163 ◽  
Author(s):  
Diego J. Bentivegna ◽  
Reid J. Smeda ◽  
Cuizhen Wang
Keyword(s):  
Spatial Resolution ◽  
Native Species ◽  
Near Infrared ◽  
Bare Soil ◽  
Flowering Plants ◽  
Hyperspectral Images ◽  
Accurate Assessment ◽  
Water Classification ◽  
Management Techniques

AbstractCutleaf teasel is an invasive, biennial plant that poses a significant threat to native species along roadsides in Missouri. Flowering plants, together with understory rosettes, often grow in dense patches. Detection of cutleaf teasel patches and accurate assessment of the infested area can enable targeted management along highways. Few studies have been conducted to identify specific species among a complex of vegetation composition along roadsides. In this study, hyperspectral images (63 bands in visible to near-infrared spectral region) with high spatial resolution (1 m) were analyzed to detect cutleaf teasel in two areas along a 6.44-km (4-mi) section of Interstate I-70 in mid Missouri. The identified classes included cutleaf teasel, bare soil, tree/shrub, grass/other broadleaf plants, and water. Classification of cutleaf teasel reached a user's accuracy of 82 to 84% and a producer's accuracy of 89% in the two sites. The conditional κ value was around 0.9 in both sites. The image-classified cutleaf teasel map provides a practical mechanism for identifying locations and extents of cutleaf teasel infestation so that specific cutleaf teasel management techniques can be implemented.Cutleaf teasel is an exotic weed that infests roadside environments in Missouri. As a growing biennial, the plant develops as a rosette during the first year and bolts during the second. Dense patches contain flowering plants with understory rosettes. The objective of this work was to develop approaches for detecting cutleaf teasel patches with accurate assessment in a complex of species along a roadside. Thus, management of cutleaf teasel could be located at specific sites. Two hyperspectral images (63 bands with 1-m spatial resolution) were analyzed to detect cutleaf teasel along the Interstate Highway I-70 in mid Missouri. Classification of cutleaf teasel reached a user's accuracy of 82 to 84% and a producer's accuracy of 89% at the two sites. The image-classified teasel map provides a practical mechanism for identifying the locations and extents of cutleaf teasel infestation so that specific management techniques can be implemented.

scholarly journals MAPPING OF FOLIAR CONTENT USING RADIATIVE TRANSFER MODELING AND VIS-NIR HYPERSPECTRAL CLOSE-RANGE REMOTE-SENSING

2015 ◽  
Vol XL-3/W3 ◽  
pp. 467-472 ◽  
Author(s):  
S. Jay ◽  
R. Bendoula ◽  
X. Hadoux ◽  
N. Gorretta
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Radiative Transfer Model ◽  
Integrating Sphere ◽  
Leaf Angle ◽  
Transfer Model

Most methods for retrieving foliar content from hyperspectral data are well adapted either to remote-sensing scale, for which each spectral measurement has a spatial resolution ranging from a few dozen centimeters to a few hundred meters, or to leaf scale, for which an integrating sphere is required to collect the spectral data. In this study, we present a method for estimating leaf optical properties from hyperspectral images having a spatial resolution of a few millimeters or centimeters. In presence of a single light source assumed to be directional, it is shown that leaf hyperspectral measurements can be related to the directional hemispherical reflectance simulated by the PROSPECT radiative transfer model using two other parameters. The first one is a multiplicative term that is related to local leaf angle and illumination zenith angle. The second parameter is an additive specular-related term that models BRDF effects. <br><br> Our model was tested on visible and near infrared hyperspectral images of leaves of various species, that were acquired under laboratory conditions. Introducing these two additional parameters into the inversion scheme leads to improved estimation results of PROSPECT parameters when compared to original PROSPECT. In particular, the RMSE for local chlorophyll content estimation was reduced by 21% (resp. 32%) when tested on leaves placed in horizontal (resp. sloping) position. Furthermore, inverting this model provides interesting information on local leaf angle, which is a crucial parameter in classical remote-sensing.

CHEMOMETRIC STRATEGIES FOR NEAR INFRARED HYPERSPECTRAL IMAGING ANALYSIS: CLASSIFICATION OF SEED GENOTYPES

2021 ◽  
Author(s):  
Simone Simões ◽  
Priscilla Rocha ◽  
Everaldo Paulo Medeiros ◽  
Carolina Silva
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Agricultural Sector ◽  
Hyperspectral Images ◽  
Imaging Analysis

Hyperspectral images have been increasingly employed in the agricultural sector for seed classification for different purposes. In the present paper we propose a new methodology based in HSI in the...

scholarly journals Spectral Unmixing for the Classification of Hyperspectral Images at a Finer Spatial Resolution

2011 ◽  
Vol 5 (3) ◽  
pp. 521-533 ◽  
Author(s):  
Alberto Villa ◽  
Jocelyn Chanussot ◽  
Jón Atli Benediktsson ◽  
Christian Jutten
Keyword(s):  
Spatial Resolution ◽  
Spectral Unmixing ◽  
Hyperspectral Images

Unsupervised methods for the classification of hyperspectral images with low spatial resolution

2013 ◽  
Vol 46 (6) ◽  
pp. 1556-1568 ◽  
Author(s):  
A. Villa ◽  
J. Chanussot ◽  
J.A. Benediktsson ◽  
C. Jutten ◽  
R. Dambreville
Keyword(s):  
Spatial Resolution ◽  
Hyperspectral Images ◽  
Unsupervised Methods

scholarly journals Classification of Hyperspectral Images with High Spatial Resolution

2018 ◽  
Vol 11 (1) ◽  
pp. 69-76
Author(s):  
Pavel V. Melnikov ◽  
◽  
Sergey A. Rylov ◽  
Igor A. Pestunov ◽  
◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Hyperspectral Images

scholarly journals The Impact of Spatial Resolution on the Classification of Vegetation Types in Highly Fragmented Planting Areas Based on Unmanned Aerial Vehicle Hyperspectral Images

2020 ◽  
Vol 12 (1) ◽  
pp. 146 ◽  
Author(s):  
Miao Liu ◽  
Tao Yu ◽  
Xingfa Gu ◽  
Zhensheng Sun ◽  
Jian Yang ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Spatial Resolution ◽  
Classification Accuracy ◽  
Hyperspectral Images ◽  
Vegetation Types ◽  
Feature Parameters ◽  
Aerial Vehicle ◽  
The Impact ◽  
Fine Classification

Fine classification of vegetation types has always been the focus and difficulty in the application field of remote sensing. Unmanned Aerial Vehicle (UAV) sensors and platforms have become important data sources in various application fields due to their high spatial resolution and flexibility. Especially, UAV hyperspectral images can play a significant role in the fine classification of vegetation types. However, it is not clear how the ultrahigh resolution UAV hyperspectral images react in the fine classification of vegetation types in highly fragmented planting areas, and how the spatial resolution variation of UAV images will affect the classification accuracy. Based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (S185) onboard a UAV platform, this paper examines the impact of spatial resolution on the classification of vegetation types in highly fragmented planting areas in southern China by aggregating 0.025 m hyperspectral image to relatively coarse spatial resolutions (0.05, 0.1, 0.25, 0.5, 1, 2.5 m). The object-based image analysis (OBIA) method was used and the effects of several segmentation scale parameters and different number of features were discussed. Finally, the classification accuracies from 84.3% to 91.3% were obtained successfully for multi-scale images. The results show that with the decrease of spatial resolution, the classification accuracies show a stable and slight fluctuation and then gradually decrease since the 0.5 m spatial resolution. The best classification accuracy does not occur in the original image, but at an intermediate level of resolution. The study also proves that the appropriate feature parameters vary at different scales. With the decrease of spatial resolution, the importance of vegetation index features has increased, and that of textural features shows an opposite trend; the appropriate segmentation scale has gradually decreased, and the appropriate number of features is 30 to 40. Therefore, it is of vital importance to select appropriate feature parameters for images in different scales so as to ensure the accuracy of classification.

scholarly journals Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images

2020 ◽  
Vol 10 (20) ◽  
pp. 7313
Author(s):  
Honglyun Park ◽  
Namkyung Kim ◽  
Sangwook Park ◽  
Jaewan Choi
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Near Infrared ◽  
Spectral Distortion ◽  
Swir Band ◽  
Sentinel 2A ◽  
Types Of Information ◽  
Shortwave Infrared ◽  
Distortion Reduction

Compared to using images in the visible and near-infrared (VNIR) wavelength range only, remotely sensed satellite imagery from the spectral wavelengths of both VNIR and shortwave infrared (SWIR), such as Sentinel-2A and Worldview-3, is more effective for analyzing various types of information for tasks such as land cover mapping, environmental monitoring and land use change detection. In this manuscript, a new sharpening technique to enhance the spatial resolution of Worldview-3 satellite imagery with various spatial and spectral resolutions is proposed. Selected and synthesized band schemes were used to produce optimal panchromatic images; then, sharpened images were generated by applying the Gram-Schmidt adaptive (GSA) and Gram-Schmidt 2 (GS2) techniques, which are component substitution (CS)- and multiresolution analysis (MRA)-based algorithms, respectively. In addition, to minimize the spectral distortion of the initial sharpened image, a postprocessing methodology for spectral distortion reduction was developed. Qualitative and quantitative evaluation of the sharpened images showed that the pansharpening performance using the GS2 technique based on the selected band scheme and spectral distortion reduction was the best. To confirm the usability of the SWIR band, supervised classification based on machine learning was performed on the pansharpened images obtained by applying the technique proposed in this study and on the pansharpened images obtained by the VNIR bands only. The classification accuracy of the results using SWIR bands was higher than that of VNIR bands only. In particular, it was confirmed that the accuracy of the classification of artificial facilities known to be effective for SWIR bands was greatly improved.

Development of robust models for rapid classification of microplastic polymer types based on near infrared hyperspectral images

2021 ◽  
Author(s):  
Tomo Kitahashi ◽  
Ryota Nakajima ◽  
Hidetaka Nomaki ◽  
Masashi Tsuchiya ◽  
Akinori Yabuki ◽  
...  
Keyword(s):  
Near Infrared ◽  
Rapid Identification ◽  
Hyperspectral Images ◽  
Small Particles

Robust models that are capable of classifying polymer types could be built based on HSI data for small particles measured on wet filters. HSI techniques with appropriate models allow the rapid identification of microplastics.

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